Learning from user interactions has a long tradition in the field of recommender systems [1]}, [2]}, [3]}, [4]}, [5]}.
A common approach is to cast the recommendation problem as a rating prediction task where the goal is to predict the ratings a user would give to items [1]}, [5]}, [4]}.
However, one should not straightforwardly base the optimization on the ratings observed in interaction logs as they are often not representative of actual user preferences due to selection bias [4]}, [5]}.
The most well-known and well-studied form of bias in rating data is popularity bias: a small selection of popular items receives the large majority of user ratings and is thus grossly overrepresented [11]}, [2]}, [13]}.
Positivity bias is also prevalent: users may be more likely to leave a rating on items they like; consequently, low ratings become underrepresented in the resulting interaction data [2]}.
As seen in Figure REF  (top),
if popular items that attract the majority of ratings also happen to be more liked by users (i.e. receive higher ratings), the average rating estimate from observed ratings becomes skewed towards a higher value.
[3]} showed that not accounting for selection bias makes the optimization procedure focus on statistically overrepresented user-item combinations.
As a solution, they propose to use IPS to unbiasedly estimate and optimize the performance of a system [16]}.
Debiasing for recommendation has become a very popular topic in recent years:
for example,
[17]} propose a doubly robust estimator that combines IPS with regression estimates;
[18]} adapted the IPS matrix factorization approach to debias click data instead of ratings;
and
[19]} incorporate a prior distribution to avoid overestimation during prediction.
